SSE Riga Student Research Papers

2016 : 9 (185)

THE REACTION OF THE CEE FINANCIAL MARKETS

TO THE POLICIES OF THE FEDERAL RESERVE

Authors: Jaak Ennuste

Tadas Gedminas

ISSN 1691-4643

ISBN 978-9984-822-16-7

November 2016

Riga

The Reaction of the CEE Financial Markets to the Policies

of the Federal Reserve

Jaak Ennuste

and

Tadas Gedminas

Supervisor: Agnes Lubloy

November 2016 Riga

1

Acknowledgements

The authors would first like to acknowledge people that helped the progress of this paper.

We would like to express gratitude to Agnes Lubloy, our supervisor, whose comments were

always very detailed and timely. Our discussions about the thesis were always constructive as we

received encouragement for parts that were well-developed, and suggestions for places where the

authors could improve. We would also like to thank Elina Ribakova for giving feedback at early

stages of the writing process. Finally, we would like to thank Arturs Pleišs and Tudor Poiană for

their comments and ideas during presentation of our thesis.

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Abstract

This paper considers the reaction of financial markets in Central Eastern Europe (CEE) to

the actions of the Federal Reserve of the United States (FED). The study covers years from 2000

until 2015, and analyses both equity and bond markets. The authors first estimate how surprising

each FED decision was over the sample period by using federal funds futures and Eurodollar

contracts. Using these calculations, an event study of the most surprising events is performed,

followed by a regression analysis which determines the average relationship between FED events

and the reaction of financial markets in CEE. The authors find that in the CEE area, stock

markets react to FED announcements, but bond markets do not. In the sample of eight countries,

on average five equity markets reacted significantly to unexpected changes in effective federal

funds rate. The reactions are stronger in the post-crisis period, when monetary policy was more

aggressive.

Keywords: the Federal Reserve, CEE financial markets, expectations, surprise factor, event study

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Table of Contents

1. Introduction......................................................................................................................................... 3

1.1. The policy of the FED during the time of study ........................................................................... 4

2. Literature Review ............................................................................................................................... 6

2.1. The impact of the FED’s policies on stock markets ..................................................................... 6

2.2. Empirical evidence of domestic and foreign effects of Quantitative Easing.............................. 9

2.3. Methodology used in the literature.............................................................................................. 10

3. Methodology ...................................................................................................................................... 11

3.1. Monetary policy surprise factors................................................................................................. 12

3.2. Event study .................................................................................................................................... 14

3.3. Regression analysis ....................................................................................................................... 16

3.4. Data ................................................................................................................................................ 17

4. Surprise factors ................................................................................................................................. 17

4.1. Most significant events.................................................................................................................. 20

5. Results ................................................................................................................................................ 21

5.1. Stock market reaction................................................................................................................... 21

5.2. Bond market reactions.................................................................................................................. 24

5.2.1. Short term bonds....................................................................................................................... 24

5.2.2. Long term bonds ....................................................................................................................... 25

6. Discussion of results .......................................................................................................................... 26

7. Limitations......................................................................................................................................... 27

8. Conclusions ........................................................................................................................................ 28

9. References .......................................................................................................................................... 30

10. Appendices..................................................................................................................................... 34

Appendix A. Estimation of scaling coefficients. ....................................................................................... 34

Appendix B. Data sources.......................................................................................................................... 36

Appendix C. Stock market results and returns on the dates with most surprising events ....................... 37

Appendix D. Short term bonds results and returns on dates with the biggest surprise factor ................ 39

Appendix E. Long term bonds results and returns on events with the highest surprise factor............... 40

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1. Introduction

The Federal Reserve is the most important central bank in the world and its policies are

not limited to the borders of the US. Their decisions often have an impact on many other

countries as well. For example, a small change in US interest rates can lead to significant capital

movements, which in turn change prevailing interest rates abroad. This paper seeks to find out

whether stock and bond markets react to the FED’s decisions in the CEE area.

More specifically, this paper analyzes how financial markets react to unexpected changes

in the federal funds rates. The federal funds rate is the rate at which banks can borrow reserves

from other banks. The FED is the body that controls this rate and its importance lies in the fact

that it determines many other interest rates and financial asset prices. Financial markets react

very quickly to events such as decisions by the FED. A transaction of selling a US treasury bill

and buying a Lithuanian government’s bond happens instantaneously. Thus, the effect of

monetary policy on financial markets is quick, contrary to the real economy where prices, wages

and employment change slowly. The study also covers a period when the FED pursued a large

scale open market operation - quantitative easing (QE). It is a program during which a central

bank buys private and public bonds from the open market, mostly from private banks. As a

result, prices of bonds increase and the interest rates decrease. In general, a central bank’s aim is

to provide low, stable inflation and high employment rates. Those effects will take place in the

long run and are not studied in this paper.

The focus of this study are the financial markets of the Central Eastern Europe (CEE). In

this study Central Eastern Europe consists of: the Czech Republic, Estonia, Hungary, Lithuania,

Latvia, Poland, Slovakia and Slovenia. The countries are chosen due to their historical similarity,

as they were part of the Eastern Bloc and since their economies and financial markets started to

develop at the same time. CEE is also a term the OECD uses for grouping these countries

(OECD, 2001).

The impact of the FED’s policies has been studied before. It has been shown that their

actions have domestic and international effects (Hausman, Wongswan (2006), Kishor, Marfatia

(2012). However, most studies have concentrated on the US, emerging markets and developed

European countries, leaving CEE region outside the scope of academic research. Furthermore,

4

few papers have considered how international markets react to changes in expectations in the

post-crisis period, leaving an important gap in the literature.

It would be reasonable to assume that investors both in the US and in the CEE follow

news announcements about the FED’s monetary policy. However, the question becomes whether

or not investors in the CEE incorporate this information in their decision making. On one side,

CEE financial markets are not highly developed, hence it might be the case that they are

insulated from outside monetary policy shocks. On the other hand, in the past years the global

financial markets have become more interconnected, leaving very few safe heavens, where one

could escape cross-border effects.

Taking this into account, this study aims to answer the following research question: Do

CEE financial markets react to the FED’s monetary policy decisions? In order to answer the

research question the authors use federal funds futures contracts and Eurodollar contracts to

estimate how surprised the markets were by the decisions of the FED. The events are Federal

Open Market Committee (hereinafter FOMC) meetings where decisions about monetary policy

are made, post-meeting minute releases and others. Then the authors assess whether the CEE

markets reacted to those events by using event study analysis and time series regressions models.

The work is structured as follows. The section below includes an overview of the FED’

policy over the sample period. Section 2 describes the literature about the FED’s impact on

financial markets in domestic and foreign markets. Section 3 provides a description of

methodology of data collection and analysis. Section 4 presents the result analysis related to

surprise factor calculations, while section 5 covers the result analysis of effects on stock and

bond markets. Section 6 discusses the key takeaways of the result analysis. Section 7 provides

limitations of the study. Finally, Section 8 provides broader conclusions about the study.

1.1. The policy of the FED during the time of study

With the aim of providing context about the time period the authors analysed, this

paragraph describes the major events that happened during this time and the way the FED

reacted. This paper covers about 16 years of monetary policy by the FED and it can be broadly

divided into three periods: the aftermath of the tech bubble and build up to the crisis, the reaction

to the Great Recession and the era of quantitative easing. In order to give a snapshot of the time

period, the authors include data from the Federal Reserve Bank of St. Louis which shows the

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effective federal funds rate over time (Figure 1). It is the rate at which banks lend reserves to one

another overnight. It is lowered during recessions and increased when there is a threat of an

overheating economy.

Figure 1. Effective Federal Funds Rate based on data from the Federal Reserve Bank of St. Louis. Created by the authors.

After the tech bubble in 2000, the FED started cutting rates in order to boost economic

activity. The recession had been sharp but brief and thus it seemed that the FED had reacted to

the economic circumstances correctly. Later, signs of high leverage became apparent in the

economy. As it can be observed on the graph, in 2005 the FED was already increasing interest

rates to limit excessive borrowing. But the roots of the crisis were deep and merely increasing

the federal funds rate would not keep the crisis from happening. The housing market of the US

collapsed and the FED needed to cut interest rates once again in order to prevent the recession

from doing more damage to the economy. In December 2008 the federal funds rate target was set

to be between 0% and 0.25%. While those interest rates stayed there for a very long time, it does

not mean that the FED took no action. In order to boost the economy, the FED completed three

rounds of quantitative easing from 2009 to 2014. The aim was to bring down long term interest

rates (Labonte, 2016). During that time the size of their balance sheet expanded significantly. For

comparison, until 2008 the size of their balance sheet was less than 1$ trillion, as shown in

Figure 2. It grew to almost 4.5$ trillion by early 2016 (Labonte, 2016).

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

Per

cen

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Effective Federal Funds Rate

6

Figure 2. Value of the FED's assets. Data from the Federal Reserve Bank of St. Louis. Created by the authors.

Although the economy had improved and the FED expected to achieve full employment

in 2016, raising interest rates was postponed for a long time, as there was disagreement about the

slack in the economy. Finally, the interest rates were increased in December 2015 (Labonte,

2016).

All in all, the period that the authors study has seen a mild recession, a period of stable

interest rate increases and the biggest financial crisis since the Great Depression. The authors of

this paper analyze whether or not the CEE financial markets have reacted to fluctuations of the

interest rates and the expanding balance sheet of the FED.

2. Literature Review

2.1. The impact of the FED’s policies on stock markets

The policies of the FED and their impact on the stock market has been studied quite

thoroughly. Bernanke and Kuttner (2004), using federal funds futures, estimated the impact that

unexpected changes in effective federal funds rate had on the stock market. They find that by

unexpectedly cutting the federal funds rate by 25 basis points (0.25%), the stock market on

0

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1000000

1500000

2000000

2500000

3000000

3500000

4000000

4500000

5000000

Mil

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ns

of

US

D

Value of the FED's assets

7

average gained 1%. The authors explain that this comes through two channels – expected

dividends and excess equity returns. Rigobon and Sack (2002) also write about the impact of the

federal funds rate changes to the stock market. Their findings are similar to Bernanke’s and

Kuttner’s, but they also found that stock markets are affected significantly on days of FOMC

meetings and other important monetary policy events, such as the Chairman’s speeches to

Congress. The same events, on the other hand, did not have a significant impact on longer-term

interest rates. The effect on those interest rates was assessed through the use of Eurodollar

futures contracts which can mature more than one year from the event that was studied.

The FED’s policy seems to affect US stock markets, but does the same hold for foreign

countries? Hausman and Wongswan (2006) use federal funds futures and Eurodollar futures to

gauge the surprise factors of FOMC meetings to assess whether unexpected changes have an

impact on foreign stock markets. Their sample consists of 49 countries, a mix of highly

developed and less developed countries. The authors find that international stock markets react to

FOMC decisions and the strength of the reaction depends on how developed the financial

markets are and how interconnected they are to the US. This research was completed in 2006 and

thus did not include the aftermath of the Great Recession. Kishor and Marfatia (2012) studied the

same topic and their data included stock market returns until 2008. They found that emerging

markets react to the FED’s policy as strongly as European markets. Their sample included only

developed European states. A surprising finding was that while at normal times a cut in fed funds

rate leads to an increase in stock prices, during times of crisis the effect is the opposite. Overall

the two studies indicate that the FED’s policies have an impact on foreign stock markets as well.

However, the sample included mostly developed countries and did not include the post-crisis

period.

One of the most common policy tool used by the FED is an open market operation

(hereinafter OMO). During an OMO the FED buys securities from the private market and thus

changes the level of reserves that the commercial banks have, in order to affect the interest rates.

This decision is made by the FOMC, who meets approximately 8-9 times per year. Theoretical

foundations of why an OMO as a monetary policy instrument would work was first articulated

by Modigliani and Sutch (1966). While the FED also controls the fed funds rate through OMO’s,

in this study, the authors consider a policy which is called quantitative easing (QE). It is a large

scale OMO during which the FED tries to bring down long term interest rates. In the case of QE,

8

the FED usually buys government bonds and mortgage backed securities from private banks and

gives them reserves in return. The result is an asset-swap which does not increase the net wealth

of the private banks. Instead it results with higher demand for government bonds which lowers

the interest rates and pushes up bond prices (Roche C.O., 2014). It is important to understand the

technical side of this process because QE is often referred to as “money printing”, but this

terminology provides a false understanding of the process.

The purchase of bonds from secondary markets should result in higher investment, stock

market valuations and investor confidence, due to the wealth effect and change in discount rates.

The wealth effect occurs when investors perceive themselves to be wealthier due to increased

asset prices, and their confidence in the economy increases. The discount rate effect comes from

the fact that the Treasury bond yield (risk free rate) decreases as the demand for those bonds

increases. As a result, the required return on equity and debt will be lower and future cash flows

will be discounted at a lower rate. The effect then transmits into output, employment and prices

(Akhtar, 1997, Bernanke, Kuttner, 2004). QE can also change expectations of the future state of

the economy and the exchange rate of the USD versus other currencies (Roche C.O., 2014).

The scale and size of the FED’s policy is not the only factor that explains changes in

financial markets. The communication and surprise factor of the policy matters as well. For

example, Wright (2011), using Treasury future derivatives around the QE announcement days

was able to estimate the magnitude of how unexpected the announcements were. Bernanke and

Kuttner (2004) hypothesize that unanticipated rate changes affect the stock market more than

anticipated ones. This indicates that if the FED’s actions are predictable, the impact should be

minimal. This introduces a new element into the FED’s work: communicating its intentions and

decisions clearly so that the markets would know how to incorporate information into prices. If

the communication is unclear, surprises are bound to happen and during times of crisis, it is not

favorable. Hayo and Neuenkirch (2010) found that speeches given by officials from the Federal

Reserve explain target rate decisions rather significantly. The notion is sometimes called

“forward guidance”, as the FED communicates clearly its intentions about target rates and gives

confidence to the investors that there will be no unanticipated moves. Following this conclusion,

this paper concentrates on the FED’s unexpected decisions, as the predictable decisions are

priced into markets beforehand. Furthermore, the authors hypothesize that financial markets will

react to unexpected events more significantly.

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2.2. Empirical evidence of domestic and foreign effects of Quantitative Easing

To this day, most of the empirical studies have looked into short term effects of the

FED’s recent policies. This is due to the fact that unconventional monetary policy has been

implemented only recently with unclear effects. Among the studies that analyzed short term

effects, Krishnamurthy and Vissing-Jorgensen (2011) found that buying mortgage backed

securities (MBS) lowered MBS yields and did the same for corporate yields. Purchases of US

treasuries also lowered treasury rates, which are considered to be “risk-free”. This effect resulted

in lowered discount rates and higher asset prices. Moreover, Gagnon et al. (2011) show that

around the announcement period of QE, long-term treasury and mortgage backed security yields

were affected significantly. On the other hand, Villanueva (2015) shows that while QE pushed

up stock prices, it is not the sole and most important factor. Indeed, strong stock fundamentals

were also supportive of the increase in stock prices.

Aside from domestic effects of QE, one might consider whether the FED’s policy had

unintended international consequences. Even though the financial crisis of 2007-2008 damaged

global financial markets, the interlinkages between still remained important. One of the first

papers to consider international effects of QE is Neely (2015). This paper showed that

government bond yields, stock market indices and exchange rates in advanced economies

(Australia, Canada, Germany, Japan and UK) moved above their historic norms during important

QE announcements. An important conclusion of this work is that the FED’s announcements to

expand QE had a stronger impact on bond yields than announcements to limit QE. In support of

these transmission effects to other markets, Glick and Leduc (2012) found QE announcements

affecting commodity prices via a signaling channel. In other words, their findings suggest that

aggressive QE announcements were associated with a negative future economic outlook,

resulting in negative impact on commodity prices. Finally, Bauer and Neely (2014) find that QE

had a non-uniform impact on the financial markets of advanced economies. Thus, decisions

about increasing the scale of quantitative easing could mean a negative effect on the CEE

markets, as it can be interpreted as a more negative outlook on the world economy.

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More recently many studies have attempted to analyze the effect that the FED’s policy

had on the emerging market economies (EME). Ahmed and Zlate (2014) find that QE had a

significant positive impact on capital inflows to the EME (the sample included Brazil, China,

India, Russia and South Africa, among others). As QE lowered interest rates in the US, investors

sought for higher yield in other countries. This consequently led to a creation of an additional

layer of interconnectedness. Chen et al. (2015) also showed that the international spillovers of

QE to stock markets were much stronger in EMEs than in advanced economies. Similarly, Kang

and Suh (2015) find that the reversal of the FED’s stance on monetary policy (from expansionary

to contractionary) resulted in negative effects to EMEs, especially to those that experienced

larger capital inflows during QE. Other studies, similarly to the case of advanced economies,

have found that the impact to EMEs is not homogeneous. Chen et al. (2014) find that those

EMEs which had stronger macroeconomic fundamentals and more liquid financial markets were

better equipped to deal with the FED’s monetary policy shock. The effect of QE on the emerging

markets has been studied quite extensively, but to the authors’ knowledge, the surprise factor of

different QE announcements has not been taken into account. The authors consider the surprise

factor during the QE period in this paper, adding novelty to the subject. Also, the studies that

already exist have considered a couple of countries which are in CEE, but this study considers all

of them.

2.3. Methodology used in the literature

There are two major methodological approaches that are used in analysing the impact of

the FED’s decisions. The first approach is to use time series models. The motivation behind

using this method is to account for movements in the markets before the actual decision is made

and for interlinkages within the financial sector. For example, different versions of vector

autoregressive (VAR) and vector error correction models (VECM) have been used to analyse the

international effects of the FED’s policy (Bauer and Neely, 2014, Chen et al., 2015). In addition,

Kang and Suh (2015) have used panel fixed effect regression models to account for time

invariant differences across countries in analysing international effects. The difference in choice

of the approach stemmed from the financial indicators that were being studied, for example CDS

spreads or interest rates.

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However, assuming that the impact of the FED’s decisions is priced into financial assets

quickly, some have argued in favor of using the event study methodology. Among others,

Gagnon et al. (2011) used the event study method to analyse the domestic effects of QE. Neely

(2015) applied this approach when analysing the effects of FED’s policy on developed

international markets, whereas Glick and Leduc (2012), using the same methodology, analysed

the impact on commodity markets. The event study method is also used when analysing policy

impact of other central bank interventions. Among others, Joyce et al. (2011) looked into the

impact of the Bank of England’s QE program, whereas Falagiarda and Reitz (2015) analysed the

European Central Bank’s policy impact. In most cases researchers analyzed how the financial

indicators such as bond yields changed 1 or 2 days after the event.

Apart from direct effect analysis, others have tried to measure the surprise factor of

FED’s policy. The surprise factors would indicate how unexpected the decision of the FED was

to financial markets. As investors price in their expectations to stock and bond prices,

unexpected decisions from the FED will make those prices fluctuate. To uncover the surprise

factor it is common to use the principal component analysis (PCA). One of the first papers to

consider using this approach is Gürkaynak, et al. (2005). The authors of this paper used the

federal futures contracts of the current month and 3-month ahead contract of each FED event in

combination with Eurodollar futures for two, three and four quarters ahead. Once the futures

contract price movements are identified, PCA analysis is used to find common factors in these

price movements. These factors are separated into two factors: target and path, where target

factor is related to changes in current federal funds rate and path factor accounts for future

monetary policy path. More recent papers which used this methodology (Doh and Connolly

(2013) and Berge and Cao (2014) have argued that during the QE period, when federal funds

rates have reached a long lasting low point, it is important to include wider and more forward

looking contract prices, for example, taking Eurodollar futures up to ten quarters ahead of the

FED event.

3. Methodology

Methodology that was used in this study can be separated into three parts: estimation of

the FED’s monetary policy surprise factors, event study, analysing the CEE financial market

12

reaction to the most surprising FED events, and time series regression analysis, used to find more

general links between CEE financial markets and the FED’s policy surprise factors. Both event

study and time series analysis build upon the estimation of monetary policy surprise factors. The

authors have decided to use personal calculations of surprise factors to maintain consistency of

the research, but the method of calculation is based on the papers of Doh and Connolly (2013),

and Gürkaynak, et al. (2005).

3.1. Monetary policy surprise factors

Estimation of the monetary policy surprise factors is based on price movements of two

types of futures contracts: federal funds futures and Eurodollar futures. The structure of federal

funds futures is such that one party pays the contract price, while the other receives the average

effective federal funds rate over a specified month. These contracts are issued on a monthly

basis, two years before settlement, and are traded on a daily basis (CME Group, n.d.). All things

considered, the federal funds futures price should reflect market expectations about the average

effective federal funds rate over a specified month. Extending this intuition, it can be said that

movements in the contract prices should reflect changes in market expectations about the

average effective federal funds rate. For this reason, the authors used daily price movements of

these contracts around FED events to capture how unexpected the FED’s decisions were.

In addition to federal funds futures, the authors also considered Eurodollar futures.

Eurodollar futures are similar in their structure to federal funds futures, where one party pays the

contract price, while the other party pays the average three-month Eurodollar interbank time

deposit rate (Labuszewski, 2013). The advantage of including Eurodollar futures is that

compared to federal funds futures, Eurodollar futures cover more periods ahead of the FED

events. It is then possible to capture in greater detail, changes in market expectations about the

trajectory of the FED’s monetary policy. For clarity, Eurodollar contracts have nothing to do

with the euro as a currency.

Using these future contracts, the authors calculated one day price movements of these

securities around FED events. The list of FED events is based on Doh and Connolly (2013),

which includes events from 1997 up to 2013. In addition, the authors of this paper added events

for the years 2014 and 2015, following the same selection procedure as Doh and Connolly

13

(2013). These events consist of FOMC meetings, releases of FOMC meeting minute or other

events related to FED’s policy making. For each event, the authors calculate price changes of

federal funds futures contract of the month, when the event happens, and for the month when the

next FOMC meeting occurs. For Eurodollar future contracts, the authors have taken quarterly

contracts from 2-quarters up to 10-quarters ahead the event. In total 11 (2 federal funds and 9

Eurodollar futures) securities were taken for the monetary policy surprise factor calculation.

Due to the nature of federal funds contracts, one day changes in the contract price does

not reflect the full change in expectations about the federal funds rate. To illustrate this point,

consider a FED event which happened on the 21st of September. The future contract is set up so

that one party pays the average federal funds rate. However, the federal funds rate could have

only changed on the 21st of September. This means that for 21 days one party would be paying

the old rate, while for the remainder of the month, it would be paying the new rate. In this case

we are only interested in the remaining part. Thus when calculating change in expectations about

the current federal funds rate, which we denote !"#,$, we adjust the change in federal funds

futures price

!"#,$ = %&&#,$ ' &&#,$(#) *+*+(-+

(1)

where ff1,t - ff1,t-1 is the one-day price change of federal funds futures price for the current month

at event date, d1 is the day of the month when event happened and D1 is number of days in that

month (Gürkaynak, et al. 2005). A similar adjustment has to be made for calculating change in

expectations about the future federal funds rate for the next FED event, which we denote !".,$

!".,$ = /%&&.,$ ' &&.,$(#) ' !"#,$-0*0

1 *0*0(-0

(2)

where ff2,t - ff2,t-1 is the one-day price change of federal funds futures contract for the month,

when the next FOMC meeting happens, at event date, d2 is the day of the month of the next

FOMC meeting and D2 is number of days in that month (Gürkaynak, et al. 2005). For Eurodollar

futures, similar adjustment is not necessary, as these contracts are much more forward looking,

and are estimated on a quarterly basis, hence one-day price changes are used to capture change in

expectations.

Afterwards, a matrix is constructed (N x X), where rows represent FED events (N) and

columns represent calculated changes in expectations (!"#and !".) and Eurodollar futures

prices (X). In the next stage principal component analysis is used, where the previously

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described matrix is decomposed into a set of orthogonal vectors Fi, i = 1, … X, with length N. In

this case, F1 is the vector that has the highest explanatory power for the original matrix. F2, is the

vector that has the highest explanatory power for the residuals of the original matrix, after

projecting each column (X) on F1, and so on. Only the first and second principal components

were taken for further analysis. The reason for using principal component analysis in this case is

that we want to capture common movements across the federal funds futures and Eurodollar

futures contracts.

To allow for a more elaborate analysis, these two components are rescaled and rotated in

such a way, that the first component is only correlated with the federal funds futures of the

current month (!"#,$2, while the second component is correlated with remaining securities. In

this way we can define the first component as the target factor – related to surprises about the

change in expectations about the current federal funds rate. The second component can be

defined as the path factor – related to the change in the expectations about trajectory of future

monetary policy.

Mathematically, this means that we transform our matrix F of the first two principal

components (N x 2, where N is the number of events) using scaling factor matrix U (2 x 2) to

calculate target and path factor matrix Z (N x 2). In Appendix A the authors provide a more

detailed explanation of how matrix U was estimated.

3 = 45 (3)

5 = 67# 8#7. 8.

9 (4)

3.2. Event study

After identifying the surprise factors of each FED event, in the next step of the analysis

the authors ranked the most surprising events in terms of target and path factors. By taking the

most surprising events it is then possible to apply the empirical distribution method. This method

helps to determine whether CEE financial market movements around the most surprising events

were significant compared to historical norms.

The empirical distribution method has been used before in analysing the effects of FED

decisions, for example Neely (2015), Glick and Leduc in (2012). In most cases, the analysis

consists of calculating 1-day or 2-day equity market returns and bond yield changes for a sample

15

period and comparing realized returns on event dates with the estimated historical return

distribution. For the analysis of the CEE financial markets this study considered 1-day, 3-day and

5-day price movements, to reflect that it might take a longer time for CEE financial markets to

incorporate new information about the FED’s announcements.

An important consideration that requires attention is the length of the historical period to

which the observed return on event day would be compared to. Following procedure used in the

literature, the authors have chosen to take a 5 year period, beginning from 2006.07.01 to

2011.07.01. The motivation of this sample is two-fold. First, this period includes the most

significant FED events that have been found in the study sample (explained in the section

below). Second, it is common that for historical return estimation a sample of approximately 5

years is taken. The starting date of the sample is usually one year before the earliest FED event

included in the sample, while the end date is usually one year after the latest FED event.

To apply this method the authors first estimate historical 1-day, 3-day and 5-day price

changes of respective CEE equity market indices, short-term government bond yields (1-year),

and long-term government bond yields (10-year) for 5 years. For equity market returns, denoted

Rt, the authors took logarithmic differences in market indices, to retrieve continuously

compounded returns

;$ = log <>$?#,@,A ' >$2 (5)

where t is the date for which the return is calculated, Pt is the value of the market index at day t.

The historical price movements are then used to estimate an empirical distribution of past

returns for each country and for each asset class. Using the empirical distribution, observed

returns on significant events days are compared to the historical distribution and a corresponding

percentile is calculated. An example is presented in Figure 3. In the example, the returns of

Warsaw Stock Index (Poland equity market proxy) on 2008.09.29 are compared with the

historical distribution of Warsaw Stock Index returns between 2006.07.01 and 2011.07.01. The

vertical line shows where exactly in the distribution observed returns lie. The shaded area

represents the historical returns which are smaller than the observed ones. Converting shaded

area in terms of percentages, gives us the percentile of observed returns, compared to the

historical distribution.

16

Figure 3. Presentation of how the empirical distribution method would be used for analysing equity market returns and bond yield changes. The three figures show empirical distribution of 1-day, 3-day and 5-day returns. As an example, returns of Poland’s equity market index on 2008.09.29 (event date) are

presented. The vertical line indicates, where in the distribution the return is. The shaded area represents the percentage of estimated returns, which are below the event return.

3.3. Regression analysis

In the third part the authors use regression analysis to estimate the average relationship

between surprise factors and CEE financial market returns. For this purpose a time-series

regression model is constructed, where the dependent variable is the CEE financial market return

(stock market returns or change in bond yields), denoted Rit, is regressed on target and path

factors, denoted targett and patht respectively. In this analysis, the authors took daily market

returns.

!" = #$ + #%&'()*&" + #,-'&." +/0!" (6)

Following the procedure used in literature for similar analysis (see e.g. Neely (2015),

Doh and Connolly (2013), Gürkaynak, et al. (2005)), the authors used OLS estimation, where

beta coefficients for each target and path factor were used to determine, whether, on average,

CEE financial markets move in response to changes in target and path factors. In this case the

distinction between target and path factors is important, as it allows to distinguish whether CEE

financial markets react more to unexpected changes in the current federal funds rate or if the

unexpected changes in future FED’s monetary policy also matters. The regression analysis was

performed for each asset class and for every country separately.

17

The study sample that was used for the regression analysis included equity market return

data for the period between 2000.01.01 and 2016.01.01. For bond market data coverage varied

greatly across countries, the sample period for the majority of the countries was between

2008.01.01 and 2016.01.01. For this reason, the analysis of market return data was separated into

3 periods: full sample (2000-2016), pre-crisis period (2000-2007) and post crisis period (2008-

2016). This way stock market and bond markets results are comparable. An additional advantage

of using such sample separation is that it can be used to compare the results with previous works,

similar to Hausman and Wongswan (2006) where target and path factor estimations were used

for foreign effect analysis, but only covered the pre-crisis period.

3.4. Data

The list of countries that were included in the sample consists of the Czech Republic,

Estonia, Hungary, Lithuania, Latvia, Slovakia, Slovenia, and Poland (OECD, 2001). Due to lack

of data Albania, Romania and Croatia were excluded from the sample. The list of market indices

that were chosen for the study include Prague Stock Exchange Index, OMX Tallinn Index,

Budapest Stock Index, OMX Vilnius, OMX Riga, Slovak Share Index, Slovenian Blue-Chip

SBITOP Index, and Warsaw Stock Exchange Index, respectively. For bond data, the authors

took zero coupon bond yields of short-term bonds (1-year) and long-term bonds (10-year). Both

market index and bond yield data were taken at daily frequency. Contract prices for federal funds

futures and Eurodollar futures contracts were taken for the same period and also at daily

frequency. Note that each month has a unique federal funds futures contract and each quarter has

a unique Eurodollar futures contract. All of the data was retrieved from Datastream, on March

1st, 2016.

4. Surprise factors

The method of calculating the surprise factors was described more thoroughly in the

methodology part, thus in this part the authors consider only the results. The target factor

indicates unexpected movements in the current federal fund rates while the path factor measures

unexpected change in future monetary policy outlook. For the target factor, the intuition of the

18

surprise factor is following: as an event happens in a given month, then a factor of 1 means that

the implied federal funds rate for this month changed by 1 basis point (0.01%). The implied

change is derived from the change of that month’s federal funds future’s price. For the path

factor the intuition is not that clear. A surprise factor of 10 would mean a 3 basis-point change in

the Eurodollar futures contract, which matures 10 quarters ahead of the event. This is also why

the path factor seems to be more volatile compared to the target factor. Thus the surprise factors

cannot be compared one to one.

Table 1. Summary statistics of estimated target and path factors.

Target factor Path factor

Mean 0.000 0.000Min -55.709 -135.07925% -0.390 -11.054Median 0.144 2.71675% 0.692 14.219Max 25.955 71.451Standard deviation 4.306 25.058No. of events 255 255

As for the target factor, the standard deviation is 4.3 with the two highest fluctuations

being -55.7 and 25.95. Although the biggest change was a negative one, it can be observed that

most events had a positive surprise on the federal funds rate, as the median effect was 0.144.The

characteristics of the path factor are similar. The largest decrease of the expect interest rate is

bigger in absolute numbers compared to the largest increase in expected interest rates. Also, the

median path factor is a positive number, indicating that there were more events that increased the

expected interest rate. In order to give a visualized overview of the surprise factors in different

points in time, the authors include Figure 3. It is worth noting that the target factor experiences

very little fluctuation from 2010. This refers back to Figure 1, which shows that between 2010

and 2016 the effective federal funds rate was close to zero and did not change much.

In the next paragraphs the authors consider six events for target factor and five for path

factor which brought along the highest surprise factors.

19

Figure 4. Graphical representation of target and path factors changes over time. Each vertical bar

corresponds to each FED event. Surprise indexes of target and path factor are not comparable in their

absolute values. Created by the authors.

20

4.1. Most significant events

Table 2. Dates of FED events, when the target and path factors were the largest, in absolute terms.

Date Target factor Date Path factor

2008.01.22 -55.709 2008.09.29 -135.079

2008.10.07 25.955 2009.09.23 -95.546

2001.09.17 14.613 2009.06.24 -87.115

2008.09.29 -14.284 2008.09.16 71.451

2007.08.17 -7.365 2008.01.22 -69.669

2008.04.30 -6.193

The most significant target factor event took place on the 22nd January in 2008. On this

date the intermeeting press release came out which stated that the target for the federal funds rate

will be lowered 75 basis points to 3.5%. It was also stated that while the short term funding

markets have rebounded a bit, the outlook on the economy is still weak as households and

businesses struggle to find financing. The discount rate was also decreased from 4.75% to 4%

(Federal Reserve, 2008a). The next most surprising event for the markets took place on the 7th of

October in 2008 when the FOMC minutes came out which regarded the meeting they had on the

16th of September. Perhaps the more important event on that day was the speech by Chairman

Ben Bernanke who addressed the problems that the FED will face and stressed the gravity of the

situation (Federal Reserve, 2008b). The third event comes from the 17th of September, 2001. As

9/11 took place only six days before, it is reasonable to assume that the following days in

financial markets were volatile for numerous reasons and the FED’s policy was not the only

significant factor. The next two most surprising events for the markets were both intermeeting

press-releases. On the 29th of September 2008 the FED called a meeting where they announced

that they are going to provide extra liquidity to the market (Federal Reserve, 2008c). On the 17th

of August 2007 the FED expressed their concerns about the economy, but did not take any action

(Federal Reserve, 2007). On the 30th of April 2008, the sixth most surprising event, the FED cut

the federal funds rate to 2% and promised to act if economic situations get worse (Federal

Reserve, 2008d). All in all there is no obvious common characteristic among these six events. It

could be said the actions of the FED were either on a large scale or the language that they used

indicated their serious concern about the economy.

Now the authors consider five of the most surprising events regarding the path factor.

Again, from first sight it seems that the events are more volatile but due to the methodology

21

target and path surprise factors cannot be compared one to one. Two events coincide for path and

target factor. The event with the highest surprise factor was on the 29th of September 2008 when

extra liquidity was provided for the market. The next one happened on the 23rd of September

2009 when the FED noted that there are improvements in the economy but the fed funds rate will

be held at a low level for a long time and open market operations will be continued at a

significant scale (Federal Reserve, 2009a). The event on the 24th of June 2009 had a very similar

message (Federal Reserve, 2009b). Both press releases noted that low interest rates may stay

here for a long time. A similar message was also given on the 16th of September 2008 as the

FED noted that significant monetary policy tools will be used to help the economy rebound

(Federal Reserve, 2008e). The fifth most surprising event was the one that was first for the target

factor. Then the FED decided to cut the fed funds rate and the discount rate then. Altogether we

are left with 8 most surprising evets for path and target factor combined, as two of the events

coincide and 17th of September 2001 was removed due to lack of available data to perform an

event study.

5. Results

In the following paragraphs the authors will describe the results of the event studies and the

regression results which show how CEE financial markets reacted to changes in surprise factors.

In event studies the authors considered the events with the highest surprise factors. The event

study and regression analysis was performed on each asset class (stock markets, short-term and

long-term bonds) and for each country.

5.1. Stock market reactions

The analysis of stock market reaction is divided into two parts. First, event study analysis

using the most surprising FED events, followed by a time series regression analysis. More

detailed results for the stock market event study are depicted in Appendix C. The result show

how much CEE equity markets moved compared to their historical norms around the most

surprising events.

Out of the eight events with the highest surprise factors, the one that stands out for the stock

markets took place on the 7th of October, 2008. Five countries reacted significantly on that date.

22

On that day, the FED released its minutes and announced the creation of another liquidity

program. But perhaps the most important event that day was a speech by the Chairman Ben

Bernanke where he focused on the dire situation of the economy and financial markets in the

world. He described that the FED has to “address a problem of historic dimensions” (Federal

Reserve, 2008b). The sharpest positive 1-day reaction on that day was observed in Lithuania,

where returns increased by 11%, while on the same day, 1-day stock market index returns in

Hungary were -7.4%. At the same, there are two more events that are worth mentioning. The first

one took place on the 16th of September, 2008, when the FED announced that it will be taking

extreme measures to combat crisis. The second one took place on the 29th of September, 2008,

when the FED decided to provide extra liquidity to the market (Federal Reserve, 2008c). In these

two days, stock market movements are less uniform across countries, but are still noticeable.

However, one should take note that all of these 3 events occurred in a one month window, which

raises the questions whether CEE equity markets were reacting to overall global downwards

trends rather than to the FED’s decisions. The authors now present the results of the time series

regression analysis.

The detailed results of the regression analysis are presented in Table 3. Before described

results, one should take note that due to differences in national holidays and hence trading days,

the number of observations and FED events included in the sample may differ across countries.

The first observation that can be made is that equity markets, in general, reacted significantly to

changes in the target factor, but most of the effect came during the post-crisis period. Six out of

the eight countries studied reacted significantly to changes in the target factor after the crisis. In

the pre-crisis period, only Latvia’s markets reacted more significantly, while the Czech

Republic’s and Slovenia’s markets reacted less significantly. In the post-crisis period, the Baltic

countries’ stock markets reacted positively to increases in the target factor. The other markets

that were significantly affected during the post-crisis period, Poland, Hungary and the Czech

Republic, had contrary effects. In other words, stock markets reacted in different directions to

changes in the target factor.

23

Table 3. Regression (Equation 6) analysis results for equity markets returns. Values above indicate beta coefficients for target and path factors.

Values in parenthesis show the p-value of the estimated coefficients. FED events column represents how many events were part of the estimation

sample. N column shows the total number of observations. As in the case of Lithuania, all sample estimation, the interpretation of coefficient is

that a 1 unit increase in the target factor, on average, was associated with 0.046% higher stock market returns

* - coefficient significant at 10% level of significance, ** - coefficient significant at 5% level of significance, *** - coefficient significant at 1%

level of significance.

Country Value All sample (2000-2015) Pre-crisis (2000-2007) After crisis (2008-2015)

Target PathFed

eventsN Target Path

Fedevents

N Target PathFed

eventsN

LithuaniaCoef. 0.0460*** -0.0047

215 3748-0.0187 -0.0010

88 17740.0565*** -0.0074*

127 1974p-value (0.0057) (0.1135) (0.6737) (0.8057) (0.0044) (0.0781)

LatviaCoef. 0.0178 -0.0003

215 3804-0.2892*** 0.0050

88 18260.0574** -0.0049

127 1978p-value (0.4261) (0.9455) (0.0001) (0.4352) (0.0141) (0.3259)

EstoniaCoef. 0.0365** -0.0032

214 37970.0090 0.0008

87 18020.0433** -0.0061

127 1995p-value (0.0342) (0.2903) (0.8458) (0.8426) (0.0349) (0.1629)

PolandCoef. -0.0473** 0.0011

216 37820.0610 -0.0001

87 1786-0.0610*** 0.0022

129 1996p-value (0.0150) (0.7422) (0.2860) (0.9808) (0.0052) (0.6330)

HungaryCoef. -0.0912*** 0.0006

218 37680.0121 -0.0034

87 1778-0.1070*** 0.0034

131 1990p-value (0.0002) (0.8901) (0.8530) (0.5606) (0.0002) (0.5683)

Czech Republic

Coef. -0.0927*** -0.0032219 3787

0.1077* -0.007487 1786

-0.1197*** 0.0000132 2001

p-value (0.0000) (0.4123) (0.0636) (0.1610) (0.0000) (0.9929)

SloveniaCoef. -0.0220 -0.0032

195 3177-0.1323** 0.0017

66 1188-0.0159 -0.0051

129 1989p-value (0.2099) (0.3278) (0.0391) (0.6964) (0.4333) (0.2524)

SlovakiaCoef. -0.0082 -0.0012

230 4177-0.0304 -0.0018

96 2089-0.0069 -0.0009

134 2088p-value (0.6533) (0.7110) (0.5790) (0.7042) (0.7312) (0.8357)

24

When the whole time period is considered, five countries reacted significantly to changes in the

target factor, but none reacted to the path factor. As for Latvia, the markets reacted negatively in

the pre-crisis period and positively in the post-crisis period to changes in the target factor,

making the reaction of their stock markets insignificant over the whole time period. However,

most of the effect was concentrated in the post-crisis period, when monetary policy was used on

an unprecedented scale and the financial markets were quite volatile. It is hard to see a common

characteristic among countries that were affected significantly.

5.2. Bond market reactions

In this section the authors are going to consider the reaction of the bond markets in the CEE

markets to the policies of the FED. Short term and long term bonds are analyzed separately, but

in both cases the same methods were applied. The authors describe both the reactions that bond

markets had on dates with highest surprise factors and reactions they had to surprise factors on

average. An important point to keep in mind is that the time period for bonds is shorter than for

stocks. Only the post-crisis period will be considered due to lack of available data.

5.2.1. Short term bonds

In Appendix D, the authors present the results of event study for short-term bonds. It can

be observed that aside from 7th of October 2008 there were no abnormal movements in the

short-term bond markets around surprising events. However, comparison of these results is

difficult as the country sample is smaller due to lack of bond data for the particular dates.

Short-term bond regression analysis results are provided in Table 4. Overall neither the

target nor the path factor surprises had a significant impact on the bond prices in the countries

the authors considered. Only in Poland the target factor seemed to have an effect on bond prices.

Otherwise, bond prices on average did not react to statements from the Federal Reserve. The

result is a bit surprising, as the FED usually operates in open market operations by buying bonds

and lowering short-term interest rates in the US. That should affect foreign bond yields due to

foreign bonds becoming more appealing. However, we fail to find evidence for this premise.

25

Table 4. Regression (Equation 6) analysis results for short-term bond yield changes. Values above

indicate beta coefficients for target and path factors. Values in parenthesis show the p-value of the

estimated coefficients. FED events column represents how many events were part of the estimation

sample. N column shows the total number of observations. As in the case of Poland, after crisis

estimation, the interpretation of coefficient is that a 1 unit increase in the target factor, on average, was

associated with a decrease of 19 basis points in short-term bond yields.

Country ValueAfter crisis

(2008-2015)Fed

eventsN

Target Path

LithuaniaCoef. 1.2818 -0.0795

76 1214p-value (0.8763) (0.7524)

LatviaCoef. 1.5643 0.1462

68 1052p-value (0.8073) (0.4860)

PolandCoef. 0.2074* -0.0372

128 2013p-value (0.0814) (0.1404)

HungaryCoef. 0.1784 0.0026

134 2088p-value (0.2629) (0.9376)

Czech Republic

Coef. -0.2338 0.0088131 2007

p-value (0.1311) (0.7855)

SloveniaCoef. 5.6009 0.0424

75 1201p-value (0.2174) (0.7638)

SlovakiaCoef. 0.2384 -0.0528

133 2037p-value (0.2265) (0.1977)

* - coefficient significant at 10% level of significance, ** - coefficient significant at 5% level of

significance, *** - coefficient significant at 1% level of significance.

5.2.2. Long term bonds

The results of long-term bond event analysis are reported in Appendix E. The results are

similar to ones of short term bonds. Even though the sample consists of only 3 countries, returns

on the 7th of October 2008 have been abnormal compared to the historic norms. For other events,

we only find occasional significance without clear patterns.

Long-term bond regressions analysis results are presented in Table 5. In this case both the

Czech Republic’s and Slovakia’s long term bond prices seemed to react significantly to changes

in the target factor, but the direction of the reaction was different. In the Czech Republic’s case,

the bond yields decreased as the target factor increases, while for Slovakia bonds moved in the

opposite direction. All in all the bond markets seem to be quite isolated from the FED’s policies.

This is contrary to the research that has been done is the pre-crisis period in developed European

26

countries, where Hausman and Wongswan (2006) found that bond prices are affected by the path

factor. On the other hand, these finding are consistent with Rigobon and Sack (2002) findings

which show that long-term bonds do not react to the FED’s decisions.

Table 5. Regression (Equation 6) analysis results for long-term bond yield changes. Values above

indicate beta coefficients for target and path factors. Values in parenthesis show the p-value of the

estimated coefficients. FED events column represents how many events were part of the estimation

sample. N column shows the total number of observations. As in the case of Czech Republic, after crisis

estimation, the interpretation of coefficient is that a 1 unit increase in the target factor, on average, was

associated with a decrease of 19 basis points in long-term bond yields.

* - coefficient significant at 10% level of significance, ** - coefficient significant at 5% level of

significance, *** - coefficient significant at 1% level of significance.

6. Discussion of results

The results show that in CEE, equity markets react to the FED’s decisions and especially to

those that have a higher target surprise factor. As the target factor is related to the current federal

funds rate, this would imply, that stock markets are more concerned about the current rates,

rather that the rates that will be prevailing in the future. Most of the stock market reaction comes

from the post-crisis period, when monetary policy was very aggressive and three waves of QE

were launched. Given that the direction of relationship between target factor and stock markets

Country ValueAfter crisis

(2008-2015)Fed

eventsN

Target Path

LithuaniaCoef. 0.2380 -0.0942

78 1270p-value (0.9763) (0.7024)

LatviaCoef. 2.8611 0.0392

76 1205p-value (0.3582) (0.6877)

PolandCoef. 0.0139 0.0016

128 2013p-value (0.8912) (0.9422)

HungaryCoef. 0.3822 0.0129

84 1329p-value (0.8873) (0.8805)

Czech Republic

Coef. -0.1931** 0.0236131 2007

p-value (0.0185) (0.1697)

SloveniaCoef. -2.8023 -0.1051

75 1201p-value (0.6527) (0.5874)

SlovakiaCoef. 0.4478*** -0.0465*

133 2037p-value (0.0006) (0.0867)

27

is different across countries, it makes it difficult to make broad conclusions, apart from that

reaction to target factor was statistically significant.

At the same time, the bond markets, on average, do not react in either case. These results are

consistent with the findings of Rigobon and Sack (2002) who found that while equity markets

react to decisions by the FED, long term interest rates do not. In this study the authors can add

that in the CEE area, short term bonds do not react either. Following the findings of Kishor and

Marfatia (2012), where it was shown that reactions to FED events are in the opposite direction

before and after the crisis. While this applies for Latvia, it doesn’t for other countries in the

sample. This study shows that there is a difference in significance as well in the pre- and post-

crisis period. Again, one outlier is Latvia where the reactions were more significant in the pre-

crisis period.

7. Limitations

There are some limitations to the study as well. First of all, when the authors choose 1-day,

3-day and 5-day windows for the event study period, there is a risk that other news or

information will affect the results during those time windows. As a result the authors cannot

argue for causality with full conviction. Second, as the data for bond prices is rather limited, it is

difficult to assess whether or not the QE rounds or the fed funds rate changes had an effect on

CEE bond markets.

Also, it is possible that there are considerable co-movements in the markets which could

potentially affect the study. If the Latvian markets react to European wide effects, it becomes

difficult to argue over the direction of causality. A further issue with causality is that if, for

example, the FED made decisions due to major turbulences in the markets, the reason for

abnormal movements in CEE financial markets would be due to turbulences and not the FED’s

decision.

Finally, when it comes to time series regression analysis, additional control variables could

have helped in crystalizing the relationship between surprise factors and returns. Variables, such

as exchange rate flexibility, size of financial markets have been shown to have an impact.

However, since these considerations are outside the scope of this research, they were not

included.

28

8. Conclusions

The effects of the FED’s monetary policy have been thoroughly researched in the US

(Bernanke and Kuttner (2004), Rogobon and Sack (2002)) and in most other developed countries

(Neely (2015), Glick and Leduct (2012)). This paper analyzed whether financial markets in the

CEE region react to policy decisions by the FED. To guide this research, the authors set out to

answer the research question: Do CEE financial markets react to the FED’s monetary policy

decisions?

To answer the question, the authors divided the FED’s policy decisions into two

categories, those that would affect current interest rates and others that are related to future

monetary policy. The findings were that financial markets in the CEE only reacted to changes in

the current interest rates. More accurately, only stock markets react to these changes. The authors

also divided the sample time period into two parts, pre- and post-crisis. The findings were that

during the post-crisis period, stock markets reacted more significantly. That might be due to the

aggressive actions that the FED took in response to the Great Recession. Over the whole sample

period, five countries out of eight reacted significantly to changes in current interest rates. It is

worth noting that the way financial markets reacted in the CEE was not uniform. From the

countries that reacted significantly, the Baltic stock markets increased as current interest rates

were increased, while the reaction in other countries was the opposite. This may be due to the

adoption of the euro, but it is not a conclusion that can be made with full conviction. Changes in

expectations about future interest rates did not affect stock prices. Also, both long and short term

bond prices were almost entirely insulated from decisions by the FED.

To the authors’ mind, these results are important for both policymakers and investors in

CEE markets. For central bankers it is important that communication with the FED is established

and maintained so that domestic central banks could react quickly and accordingly to the FED’s

policies. For investors these results provide support that domestic stock market performance is

not only determined by idiosyncratic domestic effects, but also by foreign effects, such as the

FED’s monetary policy shocks.

As this paper sought to find whether there is a reaction in CEE financial markets to FED

events, further research could focus on the reasons why the CEE markets react as they do. There

are multiple channels through which the reaction could take place, for example exchange rates,

expected economic growth and expected interest rates in Europe. Hausman and Wongswan

29

(2006) show that markets that are integrated with the US, have exchange rates which are not

flexible and financial markets that are developed, react to the FED’s decisions more.

30

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34

10. Appendices

Appendix A. Estimation of scaling coefficients.

As mentioned in section 3.1, after principal component analysis is performed and first

two principal components are taken, denoted F1 and F2, the factors are rotated and rescaled such

that the first component is only correlated with the federal funds futures of the current month

( !",#), while the second component is correlated with remaining securities. Mathematically,

this mean that principal component matrix F is transformed into matrix Z, using scaling and

rotating coefficients in matrix U. In this section, the authors describe how these coefficients are

obtained. The method that is followed in this study is based on (Gürkaynak, et al. 2005).

$ = %& (3)

& = '(" *"(+ *+- (4)

In order to estimate scaling coefficients in matrix U, we need to construct 4 sets of

equations, which would allow to solve for 4 unknowns. The first two equations come from the

fact that the matrix U columns are normalized to have unit length. This makes the new two

factors Z1 and Z2 have unit variance. In practice, this means that

("+ . (++ = 1(7)

*"+ . *++ = 1(8)

Second, the new two factors Z1 and Z2 should remain uncorrelated and orthogonal. Hence

we arrive to the third restriction

/0$"$+) = ("*" . (+*+ = 2(9)

For the final restriction, we denote 3"as the known factor loading of !"on F1 and 3+ as the

known factor loading of !"on F2. Since

%" = "456784765 [*+$" 9 (+$+] (10)

%+ = "456784765 [("$+ 9 *"$"] (11)

This implies that:

35

3+(" 9 3"(+ = 2(12)

Using equations 7, 8, 9, and 12 to impose restrictions on scaling coefficients, we can then

fully identify matrix U. Using matrix U, we estimate matrix Z, which contains target factor,

denoted Z1 and path factor, denoted Z2. In this case target factor only correlated with !", while

path factor correlated with remaining securities

36

Appendix B. Data sources

Data Period Source Comments

Equity market performance

2000.01.01-2015.12.18

Thomson Reuters Datastream

Market indices for sample countries: Latvia (RIGSE), Lithuania (VILSE), Estonia (TALSE), Poland (WIG), Hungary(BUX), Czech Republic (PX), Slovenia (SBITOP), Slovakia (SKSM)

Short-term government bonds

2006.01.01-2016.01.01

Thomson Reuters Datastream

Short-term government bonds yieldswere taken from Thomson Reuters Zero Coupon 1-Y daily bond yields. Coverage varied across countries

Long-term government bonds

2006.01.01-2016.01.01

Thomson Reuters Datastream

Long-term government bonds were taken from Thomson Reuters Zero Coupon 10-Y daily bond yields. Coverage varied across countries

Federal Funds Futures1997 Jan -2016 Jan

Thomson Reuters Datastream

Futures contracts used to calculate surprise factors of FED events. One futures contract was retrieved for each calendar month

Eurodollar Futures1997 Q1 -2018 Q3

Thomson Reuters Datastream

Futures contracts used to calculate surprise factors of FED events. One futures contract was retrieved for each quarter.

Table B.1. List of data sources used for the study. Data from Thomson Reuters Datastream was retrieved on March 1st, 2016.

37

Appendix C. Stock market results and returns on the dates with most surprising events

Table C.1. Event study results of equity market performance during the most surprising FED events in the sample period. Historical distribution

was estimated from 2006.07.01 to 2011.07.01. Values above indicate the daily return on event day (0.01 = 1%). Values in parenthesis show the

percentage of returns that were lower than the observed return.

* - return percentile smaller than 5% or larger than 95%, ** - return percentile smaller than 2.5% or larger than 97.5%, *** - return percentile

smaller than 0.5% or larger than 99.5%.

Country Lithuania Latvia Estonia Poland

Period 1-day 3-day 5-day 1-day 3-day 5-day 1-day 3-day 5-day 1-day 3-day 5-day

2007.08.17-0.005 0.007 0.004 0.009 0.008 0.014 - - - -0.013 0.043* 0.057*

(0.25) (0.63) (0.54) (0.80) (0.69) (0.72) (0.15) (0.95) (0.96)

2008.01.220.010 0.012 0.018 -0.002 0.006 -0.006 -0.007 -0.019 -0.009 0.041** 0.028 0.066**

(0.85) (0.74) (0.76) (0.43) (0.63) (0.36) (0.25) (0.17) (0.37) (0.99) (0.88) (0.98)

2008.04.30-0.004 -0.012 -0.020 0.007 0.016 0.023 0.000 0.000 0.019 0.016 0.033 0.019

(0.32) (0.23) (0.20) (0.75) (0.81) (0.84) (0.50) (0.49) (0.74) (0.89) (0.92) (0.73)

2008.09.16-0.037** -0.018 -0.088* -0.038** 0.001 -0.018 -0.052** -0.001 -0.030 0.004 0.049* 0.029

(0.02) (0.16) (0.03) (0.02) (0.51) (0.22) (0.01) (0.46) (0.15) (0.62) (0.97) (0.82)

2008.09.290.023* -0.012 -0.187*** 0.019 -0.013 -0.144*** 0.011 -0.041 -0.120** 0.011 -0.014 -0.090**

(0.97) (0.23) (0.00) (0.92) (0.22) (0.00) (0.85) (0.06) (0.01) (0.81) (0.24) (0.02)

2008.10.070.110*** 0.121*** 0.148*** 0.092*** 0.095*** 0.124*** 0.057*** 0.024 0.024 0.002 -0.064** -0.082**

(1.00) (1.00) (1.00) (1.00) (1.00) (1.00) (1.00) (0.87) (0.80) (0.57) (0.02) (0.02)

2009.06.240.003 0.001 -0.002 -0.017 -0.006 0.024 -0.003 0.003 0.004 0.005 0.011 0.005

(0.61) (0.50) (0.42) (0.12) (0.37) (0.85) (0.38) (0.54) (0.55) (0.66) (0.67) (0.54)

2009.09.23-0.024* -0.043 -0.044 -0.046** -0.119*** -0.153*** -0.013 -0.020 -0.025 -0.004 -0.016 -0.004

(0.04) (0.05) (0.08) (0.01) (0.00) (0.00) (0.12) (0.17) (0.18) (0.36) (0.22) (0.39)

38

Table C.1 (continued). Event study results of equity market performance during the most surprising FED events in the sample period. Historical

distribution was estimated from 2006.07.01 to 2011.07.01. Values above indicate the daily return on event day. Values in parenthesis show the

percentage of returns that were lower than the observed return.

* - return percentile smaller than 5% or larger than 95%, ** - return percentile smaller than 2.5% or larger than 97.5%, *** - return percentile

smaller than 0.5% or larger than 99.5%.

Country Czech Republic Hungary Slovenia Slovakia

Period 1-day 3-day 5-day 1-day 3-day 5-day 1-day 3-day 5-day 1-day 3-day 5-day

2007.08.170.004 0.034 0.056* - - - 0.014 0.016 0.031 0.006 0.016 0.031

(0.61) (0.93) (0.96) (0.92) (0.80) (0.89) (0.87) (0.80) (0.89)

2008.01.220.081*** 0.057* 0.079** 0.033* 0.031 0.029 0.026** 0.024 0.034 0.000 0.024 0.034

(1.00) (0.97) (0.98) (0.96) (0.87) (0.81) (0.98) (0.90) (0.91) (0.61) (0.90) (0.91)

2008.04.300.031* 0.021 0.017 0.013 0.026 0.024 0.015 0.054** 0.033 0.000 0.054** 0.033

(0.97) (0.84) (0.72) (0.82) (0.85) (0.76) (0.92) (0.99) (0.90) (0.61) (0.99) (0.90)

2008.09.16-0.008 0.098** 0.077** -0.024 0.053* 0.038 -0.038** 0.063** 0.042 -0.022* 0.063** 0.042

(0.24) (0.99) (0.98) (0.07) (0.96) (0.87) (0.01) (0.99) (0.95) (0.04) (0.99) (0.95)

2008.09.290.025* 0.000 -0.105** 0.016 0.011 -0.079* 0.015 -0.003 -0.080** -0.000 -0.003 -0.080**

(0.96) (0.48) (0.02) (0.86) (0.66) (0.03) (0.92) (0.45) (0.02) (0.28) (0.45) (0.02)

2008.10.070.002 -0.060* -0.043 -0.074** -0.049 -0.110** 0.042** 0.028 0.094*** 0.000 0.028 0.094***

(0.56) (0.04) (0.10) (0.01) (0.06) (0.02) (0.99) (0.93) (1.00) (0.61) (0.93) (1.00)

2009.06.240.002 0.006 -0.007 0.013 0.007 -0.007 - - - - - -

(0.56) (0.59) (0.35) (0.82) (0.61) (0.39)

2009.09.23-0.015 -0.002 -0.038 -0.023 -0.023 -0.023 -0.002 -0.002 0.014 0.000 -0.002 0.014

(0.13) (0.43) (0.11) (0.08) (0.18) (0.23) (0.42) (0.46) (0.72) (0.61) (0.46) (0.72)

39

Appendix D. Short term bonds results and returns on dates with the biggest surprise factor

Table D.1. Event study results of short-term (10Y) bond performance during the most surprising FED events in the sample period. Historical

distribution was estimated from 2006.07.01 to 2011.07.01. Values above indicate the daily change in bond yields on event day (0.01 = 1 basis

point). Values in parenthesis show the percentage of returns that were lower than the observed return.

* - change in bond yield percentile smaller than 5% or larger than 95%, ** - change in bond yield percentile smaller than 2.5% or larger than

97.5%, *** - change in bond yield percentile smaller than 0.5% or larger than 99.5%.

Country Poland Czech Republic Slovakia Hungary

Period 1-day 3-day 5-day 1-day 3-day 5-day 1-day 3-day 5-day 1-day 3-day 5-day

2007.08.170.090 0.033 0.098 -0.003 0.016 -0.005 0.006 0.002 -0.000 0.020 0.070 0.050

(0.94) (0.69) (0.86) (0.49) (0.57) (0.48) (0.61) (0.54) (0.55) (0.75) (0.82) (0.73)

2008.01.220.013 0.036 0.121 0.138 -0.050 -0.041 0.116 0.152 -0.000 -0.010 -0.060 -0.100

(0.62) (0.70) (0.90) (0.91) (0.30) (0.37) (0.89) (0.91) (0.55) (0.37) (0.27) (0.26)

2008.04.30-0.015 0.008 0.015 -0.032 -0.041 -0.091 0.005 0.012 0.038 0.000 -0.080 -0.150

(0.36) (0.55) (0.55) (0.32) (0.32) (0.23) (0.60) (0.60) (0.70) (0.69) (0.23) (0.16)

2008.09.16-0.036 0.026 -0.030 0.013 0.126 0.094 0.006 0.105 0.087 0.070 -0.030 0.090

(0.20) (0.66) (0.34) (0.61) (0.89) (0.80) (0.61) (0.85) (0.81) (0.91) (0.42) (0.79)

2008.09.29-0.082 -0.101 -0.144 -0.044 -0.077 -0.056 0.059 -0.180 -0.448** 0.030 0.100 0.120

(0.07) (0.09) (0.08) (0.28) (0.23) (0.32) (0.80) (0.09) (0.01) (0.78) (0.86) (0.82)

2008.10.070.202** 0.192* 0.309** -0.145 0.111 0.018 0.558** 0.200 0.442** 0.270** 0.270* 0.270

(0.99) (0.97) (0.98) (0.07) (0.87) (0.56) (0.99) (0.94) (0.98) (0.98) (0.95) (0.93)

2009.06.240.006 0.050 -0.007 0.050 0.054 0.038 0.094 0.078 0.005 -0.080 -0.120 -0.150

(0.54) (0.77) (0.45) (0.75) (0.71) (0.64) (0.86) (0.80) (0.58) (0.11) (0.13) (0.15)

2009.09.23-0.029 0.007 -0.004 -0.034 -0.038 0.047 -0.178 -0.063 -0.382** 0.000 -0.050 -0.080

(0.24) (0.54) (0.46) (0.31) (0.34) (0.67) (0.06) (0.26) (0.02) (0.69) (0.32) (0.29)

40

Appendix E. Long term bonds results and returns on events with the highest surprise factor

Table E.1. Event study results of long-term (10Y) bond performance during the most surprising FED events in the sample period. Historical

distribution was estimated from 2006.07.01 to 2011.07.01. Values above indicate the daily change in bond yields on event day (0.01 = 1 basis

point). Values in parenthesis show the percentage of returns that were lower than the observed return.

* - change in bond yield percentile smaller than 5% or larger than 95%, ** - change in bond yield percentile smaller than 2.5% or larger than

97.5%, *** - change in bond yield percentile smaller than 0.5% or larger than 99.5%.

Country Czech Republic Poland Slovakia

Period 1-day 3-day 5-day 1-day 3-day 5-day 1-day 3-day 5-day

2007.08.170.011 -0.029 0.007 -0.038 0.035 0.037 -0.039 -0.022 -0.002

(0.64) (0.31) (0.54) (0.16) (0.73) (0.67) (0.22) (0.37) (0.49)

2008.01.220.042 -0.037 -0.011 0.051 -0.020 0.045 -0.209** -0.032 -0.118

(0.88) (0.27) (0.44) (0.89) (0.37) (0.71) (0.01) (0.32) (0.11)

2008.04.30-0.030 0.013 0.030 0.036 -0.061 -0.139 0.098 0.086 0.041

(0.17) (0.58) (0.66) (0.83) (0.19) (0.11) (0.93) (0.87) (0.68)

2008.09.16-0.045 -0.055 -0.053 0.022 0.117 0.136 0.053 0.146 0.075

(0.11) (0.18) (0.22) (0.74) (0.94) (0.91) (0.83) (0.95) (0.80)

2008.09.29-0.044 -0.030 0.052 -0.021 -0.180** -0.252** 0.020 -0.005 -0.055

(0.11) (0.30) (0.74) (0.29) (0.02) (0.02) (0.65) (0.46) (0.25)

2008.10.070.086* 0.158* 0.332** -0.176*** 0.068 0.123 0.160** 0.193* 0.261**

(0.97) (0.97) (0.99) (0.00) (0.83) (0.90) (0.98) (0.97) (0.98)

2009.06.24-0.084* -0.139* -0.182 -0.008 -0.009 -0.057 0.203** 0.091 0.018

(0.04) (0.05) (0.05) (0.42) (0.44) (0.27) (0.99) (0.88) (0.58)

2009.09.23-0.014 0.017 0.040 -0.083* -0.075 -0.131 -0.039 -0.089 -0.180*

(0.31) (0.61) (0.69) (0.04) (0.15) (0.12) (0.22) (0.14) (0.05)